It is an important task to identify the authenticity of paper money to maintain financial order and social public interests. Currently, there are generally two kinds of counterfeit money, that is, forged money and altered money. The forged money refers to the counterfeit money which is fabricated by imitating the pattern, shape and colour etc. of the genuine money. The altered money refers to the counterfeit money which is fabricated by dig-mending, covering, obliterating, patching, shifting, reprinting etc. on the basis of the genuine money to change the original form of the genuine money. Currently, the technology of identifying the forged money has been relatively mature in domestic. However, the altered money contains some genuine money and has some anti-counterfeiting features that the genuine money has, so it is more difficult to identify.
The relevant technology discloses an altered money identification method, including steps of: a) obtaining the entire grayscale image of the paper money to be tested; b) binarizing the entire grayscale image; and c) analyzing the binarized image data to judge the authenticity of the paper money. Wherein binarizing the grayscale image is as follows: A threshold T is set. The grayscale image data is divided into two parts by T, that is, a pixel group in which the grayscale value of each pixel is greater than T and a pixel group in which the grayscale value of each pixel is less than T. Then, the grayscale value of the pixel of the pixel group in which the grayscale value of each pixel is greater than T is set to be 255 (or set to be 0), and the grayscale value of the pixel of the pixel group in which the grayscale value of each pixel is less than T is set to be 0 (or set to be 255). The grayscale values of all the pixels in the binarized image are detected line by line firstly, then the grayscale value of each pixel of each line is compared with the grayscale value of the previous pixel adjacent to the pixel in this line one by one, to judge whether the grayscale values of the current pixel and the previous pixel adjacent thereto are different. When the grayscale values are different, the position of the current pixel (simply an abrupt changed dot for short) is recorded. When the grayscale values of all the pixels in the binarized image are detected line by line, the positions of all the abrupt changed dots detected are judged whether to comply with a preset rule, for example, the positions of all the abrupt changed dots are within columns of certain scope. When the positions of the abrupt changed comply with the preset rule, a spliced seam is judged to be present in the paper money to be tested, that is, the paper money to be tested is judged to be the altered money. When there is no abrupt changed dot or the positions of the abrupt changed dots do not comply with the preset rule, the grayscale values of all the pixels in the binarized image are detected column by column. The grayscale value of each pixel of each column is compared with the grayscale value of previous pixel adjacent to the pixel in this column one by one to judge whether the grayscale values of the current pixel and the previous pixel adjacent thereto are different. When the grayscale values are different, the position of the current pixel (simply an abrupt changed dot for short) is recorded. After the grayscale values of all the pixels in the binarized image are detected column by column, the positions of all the abrupt changed dots detected are judged whether to comply with the preset rule, for example, the positions of all the abrupt change pixels are within lines of certain scope. When the positions of the abrupt changed dots comply with the preset rule, a spliced seam is judged to be present in the paper money to be tested, that is, the paper money to be tested is judged to be the altered money. Otherwise, the paper money to be tested is judged to be the unaltered money. For the altered money with obvious altering features, for example the altered money with obvious spliced seam, the spliced seam in the image can be extracted by binarizing the grayscale image so as to implement the identification of the altered money.
However, since the grayscale values of all the pixels in the binarized image of the grayscale images are simplified to be two levels (0 or 255) from 256 levels (0-255), the adjacent pixels with the minor grayscale value difference may be very likely processed as the pixels with the same grayscale value. At this time, it is impossible to detect the difference of the grayscale values of the adjacent pixels by dot-to-dot comparison. An entire image of an altered money with a relatively obvious spliced seam is shown in FIG. 1a. As shown in this image, there is an obvious difference between the grayscale value of the pixel on the spliced seam and the grayscale value of the pixel on both sides of the spliced seam. An entire image of an altered money without obvious spliced seams is shown in FIG. 1b. As shown in this image, there is an unobvious difference between the grayscale value of the pixel on the spliced seam and the grayscale value of the pixel on both sides of the spliced seam. The entire images of two pieces of altered money as shown in FIG. 1a and FIG. 1b are binarized by the same threshold T=180. FIG. 2a is an image obtained by binarizing the entire image as shown in FIG. 1a, and FIG. 2b is an image obtained by binarizing the entire image as shown in FIG. 1b. Obviously, it may be easy to detect the spliced seam present in the altered money from the image data of the image as shown in FIG. 2a, but it is very difficult to detect the spliced seam present in the altered money from the image data of the image as shown in FIG. 2b. Therefore, it can be seen that the method for identifying the altered money by binarizing the grayscale image has a disadvantage of inaccurate detection.
No effective solution has been proposed for the problem that the altered money identification method is inaccurate in the relevant art.